mla.R

#' Supervised and unsupervised classification in Remote Sensing
#'
#' It allows to execute supervised classification
#' in satellite images through various algorithms.
#'
#' @references
#' Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. (2013).
#' An introduction to statistical learning : with applications in R. New York: Springer.
#'
#' Mountrakis, G., Im, J., Ogole, C. (2011). Support vector machines in remote sensing:
#' A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66, 247-259.
#'
#' Belgiu, M., Dragut., L. (2016). Random Forest in Remote Sensing: A Review of Applications
#' and Future Directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24-31.
#'
#' Maxwell, A.E., Warner, T.A., Fang, F. (2018). Implementation of machine-learning
#' classification in remote sensing: an applied review. International Journal of Remote
#' Sensing, 29(9), 2784-2817.
#'
#' Pradhan, R., Ghose, M.K., Jeyaram, A. (2010). Land Cover Classification of Remotely
#' Sensed Satellite Data using Bayesian and Hybrid classifier. International Journal
#' of Computer Applications, 7(11).
#'
#' Holloway, J., Mengersen, K. (2018). Statistical Machine Learning Methods and Remote
#' Sensing for Sustainable Development Goals: A Review. Remote Sensing, 10(9), 1365.
#'
#' @section Note: If \code{model = "LMT"}, the function is using "Logistic Model Trees"
#' from http://topepo.github.io/caret/train-models-by-tag.html of the \code{caret} package.
#'
#' @importFrom caret train knn3
#' @importFrom raster getValues extract predict
#' @importFrom e1071 svm naiveBayes
#' @importFrom randomForest randomForest
#' @importFrom rgeos gIntersects
#'
#' @param img RasterStack or RasterBrick
#' @param endm Signatures. Geometry type, Points or Polygons (typically shapefile),
#' containing the training data.
#' @param model Model to use. It can be Support Vector Machine (\link[e1071]{svm}) like
#' \code{model = 'svm'}, Random Forest (\link[randomForest]{randomForest})
#' like \code{model = 'randomForest'}, Naive Bayes (\link[e1071]{naiveBayes})
#' like \code{model = 'naiveBayes'}, Decision Tree (\link[caret]{train})
#' like \code{model = 'LMT'}, Neural Networks (\link[nnet]{nnet})
#' like \code{model = 'nnet'}, K-nearest Neighbors (\link[caret]{knn3}) like \code{model = 'knn'}.
#' @param training_split For splitting samples into two subsets, i.e. training data and for
#' testing data
#' @param verbose This parameter is Logical. It Prints progress messages during execution
#' @param ... Parameters to be passed to a machine learning algorithm. Please see \link[e1071]{svm}, \link[randomForest]{randomForest}, \link[e1071]{naiveBayes}, \link[caret]{train}, \link[nnet]{nnet} and \link[caret]{knn3}
#'
#' @examples
#' library(ForesToolboxRS)
#' library(raster)
#' library(snow)
#' library(caret)
#' library(sf)
#'
#' # Load the datasets
#' data(img_l8)
#' data(endm)
#'
#' # Random Forest Classifier
#' classMap <- mla(img = img_l8, endm = endm, model = "randomForest", training_split = 80)
#' print(classMap)
#'
#' # Plot image
#' plot(classMap$Classification)
#'
#' @export
#'

mla <- function(img, endm, model, training_split = 80, verbose = FALSE, ...) {
  if (!inherits(img, "Raster")) stop("img must be a RasterBrick or RasterStack", call. = TRUE)

  if (!raster::compareCRS(img, endm)) stop("img and endm must have the same projection", call. = TRUE)

  if (inherits(endm, "sf")) {
    TypeEndm <- "sf class"
  } else {
    stop("Signatures (endm) must be sf class", call. = TRUE)
  }

  algoS <- c("svm", "randomForest", "naiveBayes", "LMT", "nnet", "knn")

  algoSM <- c("svm", "randomForest", "naiveBayes", "knn")

  if (verbose) {
    message(paste0(paste0(rep("*", 10), collapse = ""), " The origin of the signatures are ", TypeEndm, paste0(rep("*", 10), collapse = "")))
  }

  vegt <- extract(img, endm)

  endm <- as.data.frame(endm)
  names(endm[1]) <- c("class")

  endm <- data.frame(vegt, class = endm[,1])

  if (model %in% algoS) {
    if (is.numeric(endm$class)) {
      endm$class <- as.factor(endm$class)
    }

    # Training and Testing
    sample_split <- sample(
      1:dim(endm)[1],
      training_split * dim(endm)[1] / 100
    )
    testing <- endm[sample_split, ]
    training <- endm[-sample_split, ]
  }

  errorMatrix <- function(prediction, reference) {

    # Confusion matrix
    MC_ini <- table(prediction, reference)
    OA <- sum(diag(MC_ini)) / sum(MC_ini)

    ua <- diag(MC_ini) / rowSums(MC_ini) * 100
    co <- 100 - ua

    pa <- diag(MC_ini) / colSums(MC_ini) * 100
    om <- 100 - pa

    MCp <- cbind(MC_ini, Total = as.vector(rowSums(MC_ini)), Users_Accuracy = as.vector(ua), Commission = as.vector(co))

    MCf <- rbind(MCp,
      Total = c(as.vector(colSums(MC_ini)), rep(NA, 3)), Producer_Accuracy = c(as.vector(pa), rep(NA, 3)),
      Omission = c(as.vector(om), rep(NA, 3))
    )

    mc <- list(MCf = MCf, MC_ini = MC_ini)

    return(mc)
  }

  if (model == "svm") {

    # Applying svm
    model_algo <- svm(class ~ ., data = training, type = "C-classification", ...)
    prediction <- predict(model_algo, testing)
  } else if (model == "randomForest") {

    # Applying randomForest
    model_algo <- randomForest(class ~ ., data = training, importance = TRUE, ...)
    prediction <- predict(model_algo, testing[, -dim(endm)[2]])
  } else if (model == "naiveBayes") {

    # Applying naiveBayes
    model_algo <- naiveBayes(class ~ ., data = training, ...)
    prediction <- predict(model_algo, testing[, -dim(endm)[2]])
  } else if (model == "LMT") {

    # Applying Logistic Model Trees
    model_algo <- train(class ~ ., method = "LMT", data = training, ...)
    prediction <- predict(model_algo, testing[, -dim(endm)[2]], type = "raw")
  } else if (model == "nnet") {

    # Applying nnet
    nnet.grid <- expand.grid(size = c(10, 50), decay = c(5e-4, 0.2))
    model_algo <- train(class ~ ., data = training, method = "nnet", tuneGrid = nnet.grid, trace = FALSE, ...)
    prediction <- predict(model_algo, testing[, -dim(endm)[2]], type = "raw")
  } else if (model == "knn") {

    # Applying knn
    model_algo <- knn3(class ~ ., data = training, k = 5, ...)
    prediction <- predict(model_algo, testing[, -dim(endm)[2]], type = "class")
  } else {
    stop("Unsupported classification method.", call. = TRUE)
  }

  if (verbose) {
    message(paste0(paste0(rep("*", 10), collapse = ""), " Model summary ", paste0(rep("*", 10), collapse = "")))
    print(model_algo)
  }

  if (verbose) {
    message(paste0(paste0(rep("*", 10), collapse = ""), " Apply model to the raster ", paste0(rep("*", 10), collapse = "")))
  }

  if (model %in% algoSM) {

    # Apply model to the raster
    raster::beginCluster(type = "SOCK")
    raster_class <- raster::clusterR(img, raster::predict, args = list(model = model_algo, type = "class"))
    raster::endCluster()
  } else {
    raster_class <- predict(img, model = model_algo)
  }

  if (verbose) {
    message(paste0(paste0(rep("*", 10), collapse = ""), " Confusion Matrix and finel result ", paste0(rep("*", 10), collapse = "")))
  }

  # Confusion matrix
  MC <- errorMatrix(prediction = prediction, reference = testing[, dim(endm)[2]])

  results <- list(
    Overall_accuracy = (caret::confusionMatrix(MC$MC_ini)$overall[1:6]) * 100,
    Confusion_matrix = MC$MCf,
    Classification = raster_class
  )

  return(structure(results, class = "mla"))
}

#' Print of the "mla" class
#'
#' @param x Object of class "mla".
#' @param ... Not used.
#'
#' @export
#'
print.mla <- function(x, ...) {
  cat("******************** ForesToolboxRS CLASSIFICATION ********************\n")
  cat("\n****Overall Accuracy****\n")
  print(x$Overall_accuracy)
  cat("\n****Confusion Matrix****\n")
  print(x$Confusion_matrix)
  cat("\n****Classification Map****\n")
  methods::show(x$Classification)
}